Federated learning enables collaborative machine learning while preserving data privacy, but high communication and computation costs, exacerbated by statistical and device heterogeneity, limit its practicality in mobile edge computing. Existing compression methods like sparsification and pruning reduce per-round costs but may increase training rounds and thus the total training cost, especially under heterogeneous environments. We propose a lightweight personalized FL framework built on parameter decoupling, which separates the model into shared and private subspaces, enabling us to uniquely apply gradient sparsification to the shared component and model pruning to the private one. This structural separation confines communication compression to global knowledge exchange and computation reduction to local personalization, protecting personalization quality while adapting to heterogeneous client resources. We theoretically analyze convergence under the combined effects of sparsification and pruning, revealing a sparsity-pruning trade-off that links to the iteration complexity. Guided by this analysis, we formulate a joint optimization that selects per-client sparsity and pruning rates and wireless bandwidth to reduce end-to-end training time. Simulation results demonstrate faster convergence and substantial reductions in overall communication and computation costs with negligible accuracy loss, validating the benefits of coordinated and resource-aware personalization in resource-constrained heterogeneous environments.

Nowadays, end-to-end video coding for both machine and human vision has become an emerging research topic. In complicated systems such as large-scale internet of video things (IoVT), feature streams and video streams can be separately encoded and delivered for machine judgement and human viewing. In this paper, we propose a deep scalable video codec (DeepSVC) to support three-layer scalability from machine to human vision. First, we design a semantic layer that encodes semantic features extracted from the captured video for machine analysis. This layer employs a conditional semantic compression (CSC) method to remove redundancies between semantic features. Second, we design a structure layer that can be combined with semantic layer to predict the captured video at a low quality. This layer effectively estimates video frames based on semantic layer with an interlayer frame prediction (IFP) network. Third, we design a texture layer that can be combined with the above two layers to reconstruct high-quality video signals. This layer also takes advantage of the IFP network to improve its coding efficiency. In large-scale IoVT systems, DeepSVC can deliver semantic layer for regular use and transmit the other layers on demand. Experimental results indicate that the proposed DeepSVC outperforms popular codecs for machine and human vision. Compared with scalable extension of H.265/HEVC (SHVC), the proposed DeepSVC reduces average bit-per-pixel (bpp) by 25.51%/27.63%/59.87% at the same mAP/PSNR/MS-SSIM. Sourcecode is available at: https://github.com/LHB116/DeepSVC.

Learning-based image coding has attracted increasing attentions for its higher compression efficiency than reigning image codecs. However, most existing learning-based codecs do not support variable rates with a single encoder; their decoders are also of fixed, high computational complexity. In this paper, we propose an End-to-end, Learning-based and Flexible Image Codec (ELFIC) that supports variable rate and flexible decoding complexity. First, we propose a general image codec with Nonlinear Feature Fusion Transform (NFFT) as nonlinear transforms to improve its Rate-Distortion (RD) performance. Second, we propose an Instance-aware Decoding Complexity Allocation (IDCA) approach, which exploits image contents for a tradeoff between reconstruction quality and computational complexity in the decoding process. Third, we propose an RD-Complexity (RDC) optimization algorithm, which maximizes the image quality under given rate and complexity constraints for the whole framework. Experimental results show that ELFIC achie-ves variable rate, flexible decoding complexity with the state-of-the-art RD performance. It also supports a more efficient decoding process by focusing on image contents. Source codes are available at https://github.com/Zhichen-Zhang/ELFIC-Image-Compression.

Rain streaks usually result in severe image visual degradation and foreground occlusion, affecting the quality of computer tasks in outdoor scenes. Currently, the mainstream methods in single-image deraining are based on data-driven. However, the deep learning network could be imperfect, with limited power for learning the global information from rain streaks all over the map. In order to solve this problem, we proposed a novel Hierarchical Distillation Network (HD-Net). In this network, Hierarchical Feature Extraction Block (HFEB) can fully utilize the Transformer's learning ability in high-level features, integrate local detail extraction and global structure representation, and compensate for the weakness of the Convolutional Neural Network (CNN), which is overattentive to the underlying image features. Furthermore, the Distillation-Calibration Block (DCB) are adopted to avoid feature redundancy during model training and calibrate the channel and spatial information through the feature transmission, which could significantly improve the learning efficiency. Finally, the experiment results show that our model performs better than traditional CNN models and state-of-the-art methods.

Hitherto, Versatile Video Coding (VVC) has a more magnificent overall performance than High Efficiency Video Coding (HEVC). The Quadtree with Nested Multi-Type Tree (QTMT) coding block structure can substantially enhance video coding quality in VVC. However, the coding gain also leads to a greater coding complexity. Therefore, this letter proposes a Fast Decision Scheme Based on Structural Similarity Index Metric Variation (FDS-SSIMV) to solve this problem. Firstly, the Structural Similarity Index Metric Variation (SSIMV) characteristic among the sub coding units of the spit mode is illustrated. Next, to evaluate the SSIMV value, SSIMV measure strategies are designed for different split modes in this letter. Then, the desired split modes are selected by the SSIMV values. Experimental results show that the proposed method achieves an average encoding Time Saving (TS) and Bjøntegaard Delta Bit Rate (BDBR) with 64.74% and 2.79%, respectively, outperforming the benchmarks.

In TCM theory, the syndrome is crucial to diagnose diseases and treat patients. In syndrome identification, the relation of symptoms usually correlates with syndrome and represents the pattern of syndrome at symptomatic level. Hence, we learn models for classifying syndromes in depression using 4 different algorithms, which are naive Bayes, Bayes network, SVM and C4.5. From the results of classification, we find that the dependence of symptoms has something to do with the accuracies of syndrome classification. Then, 8 symptom networks corresponding to depression and 7 syndromes are constructed to explore the interaction profile of symptoms under syndrome. By comparing syndrome-specific symptom network to the base network of depression, we discover the enriched edges and different nodes to represent the pattern of each syndrome. Literature and symptom ranking by Fisher score demonstrate the correctness of the different nodes selected through network comparison. After all, the enriched edges and different nodes associated with a given syndrome reveal the pattern of that syndrome at symptomatic level.